# !pip install git+https://github.com/alberanid/imdbpy
# !pip install pandas
# !pip install numpy
# !pip install matplotlib
# !pip install seaborn
# !pip install pandas_profiling --upgrade
# !pip install plotly
# !pip install wordcloud
# !pip install Flask
# Import Dataset
# Import File from Loacal Drive
# from google.colab import files
# data_to_load = files.upload()
# from google.colab import drive
# drive.mount('/content/drive')
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
import collections
import plotly.express as px
import plotly.graph_objects as go
import nltk
import re
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.probability import FreqDist
from nltk.util import ngrams
from plotly.subplots import make_subplots
from plotly.offline import iplot, init_notebook_mode
from wordcloud import WordCloud, STOPWORDS
from pandas_profiling import ProfileReport
%matplotlib inline
warnings.filterwarnings("ignore")
nltk.download('all')
[nltk_data] Downloading collection 'all' [nltk_data] | [nltk_data] | Downloading package abc to [nltk_data] | C:\Users\pawan\AppData\Roaming\nltk_data... [nltk_data] | Package abc is already up-to-date! [nltk_data] | Downloading package alpino to [nltk_data] | C:\Users\pawan\AppData\Roaming\nltk_data... [nltk_data] | Package alpino is already up-to-date! [nltk_data] | Downloading package biocreative_ppi to [nltk_data] | C:\Users\pawan\AppData\Roaming\nltk_data... [nltk_data] | Package biocreative_ppi is already up-to-date! [nltk_data] | Downloading package brown to [nltk_data] | C:\Users\pawan\AppData\Roaming\nltk_data... [nltk_data] | Package brown is already up-to-date! [nltk_data] | Downloading package brown_tei to [nltk_data] | C:\Users\pawan\AppData\Roaming\nltk_data... [nltk_data] | Package brown_tei is already up-to-date! [nltk_data] | Downloading package cess_cat to [nltk_data] | C:\Users\pawan\AppData\Roaming\nltk_data... [nltk_data] | Package cess_cat is already up-to-date! 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[nltk_data] | [nltk_data] Done downloading collection all
True
# path = '/content/drive/MyDrive/Files/'
path = 'C:\\Users\\pawan\\OneDrive\\Desktop\\ott\\Data\\'
df_movies = pd.read_csv(path + 'ottmovies.csv')
df_movies.head()
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | Language | Plotline | Runtime | Kind | Seasons | Netflix | Hulu | Prime Video | Disney+ | Type | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Inception | 2010 | 13+ | 8.8 | 87% | Christopher Nolan | Leonardo DiCaprio,Joseph Gordon-Levitt,Elliot ... | Action,Adventure,Sci-Fi,Thriller | United States,United Kingdom | English,Japanese,French | Dom Cobb is a skilled thief, the absolute best... | 148.0 | movie | NaN | 1 | 0 | 0 | 0 | 0 |
| 1 | 2 | The Matrix | 1999 | 16+ | 8.7 | 88% | Lana Wachowski,Lilly Wachowski | Keanu Reeves,Laurence Fishburne,Carrie-Anne Mo... | Action,Sci-Fi | United States | English | Thomas A. Anderson is a man living two lives. ... | 136.0 | movie | NaN | 1 | 0 | 0 | 0 | 0 |
| 2 | 3 | Avengers: Infinity War | 2018 | 13+ | 8.4 | 85% | Anthony Russo,Joe Russo | Robert Downey Jr.,Chris Hemsworth,Mark Ruffalo... | Action,Adventure,Sci-Fi | United States | English | As the Avengers and their allies have continue... | 149.0 | movie | NaN | 1 | 0 | 0 | 0 | 0 |
| 3 | 4 | Back to the Future | 1985 | 7+ | 8.5 | 96% | Robert Zemeckis | Michael J. Fox,Christopher Lloyd,Lea Thompson,... | Adventure,Comedy,Sci-Fi | United States | English | Marty McFly, a typical American teenager of th... | 116.0 | movie | NaN | 1 | 0 | 0 | 0 | 0 |
| 4 | 5 | The Good, the Bad and the Ugly | 1966 | 16+ | 8.8 | 97% | Sergio Leone | Eli Wallach,Clint Eastwood,Lee Van Cleef,Aldo ... | Western | Italy,Spain,West Germany,United States | Italian | Blondie (The Good) (Clint Eastwood) is a profe... | 161.0 | movie | NaN | 1 | 0 | 1 | 0 | 0 |
# profile = ProfileReport(df_movies)
# profile
def data_investigate(df):
print('No of Rows : ', df.shape[0])
print('No of Coloums : ', df.shape[1])
print('**'*25)
print('Colums Names : \n', df.columns)
print('**'*25)
print('Datatype of Columns : \n', df.dtypes)
print('**'*25)
print('Missing Values : ')
c = df.isnull().sum()
c = c[c > 0]
print(c)
print('**'*25)
print('Missing vaules %age wise :\n')
print((100*(df.isnull().sum()/len(df.index))))
print('**'*25)
print('Pictorial Representation : ')
plt.figure(figsize = (10, 10))
sns.heatmap(df.isnull(), yticklabels = False, cbar = False)
plt.show()
data_investigate(df_movies)
No of Rows : 16923
No of Coloums : 20
**************************************************
Colums Names :
Index(['ID', 'Title', 'Year', 'Age', 'IMDb', 'Rotten Tomatoes', 'Directors',
'Cast', 'Genres', 'Country', 'Language', 'Plotline', 'Runtime', 'Kind',
'Seasons', 'Netflix', 'Hulu', 'Prime Video', 'Disney+', 'Type'],
dtype='object')
**************************************************
Datatype of Columns :
ID int64
Title object
Year int64
Age object
IMDb float64
Rotten Tomatoes object
Directors object
Cast object
Genres object
Country object
Language object
Plotline object
Runtime float64
Kind object
Seasons float64
Netflix int64
Hulu int64
Prime Video int64
Disney+ int64
Type int64
dtype: object
**************************************************
Missing Values :
Age 8457
IMDb 328
Rotten Tomatoes 10437
Directors 357
Cast 648
Genres 234
Country 303
Language 437
Plotline 4958
Runtime 382
Seasons 16923
dtype: int64
**************************************************
Missing vaules %age wise :
ID 0.000000
Title 0.000000
Year 0.000000
Age 49.973409
IMDb 1.938191
Rotten Tomatoes 61.673462
Directors 2.109555
Cast 3.829108
Genres 1.382734
Country 1.790463
Language 2.582284
Plotline 29.297406
Runtime 2.257283
Kind 0.000000
Seasons 100.000000
Netflix 0.000000
Hulu 0.000000
Prime Video 0.000000
Disney+ 0.000000
Type 0.000000
dtype: float64
**************************************************
Pictorial Representation :
# ID
# df_movies = df_movies.drop(['ID'], axis = 1)
# Age
df_movies.loc[df_movies['Age'].isnull() & df_movies['Disney+'] == 1, "Age"] = '13'
# df_movies.fillna({'Age' : 18}, inplace = True)
df_movies.fillna({'Age' : 'NR'}, inplace = True)
df_movies['Age'].replace({'all': '0'}, inplace = True)
df_movies['Age'].replace({'7+': '7'}, inplace = True)
df_movies['Age'].replace({'13+': '13'}, inplace = True)
df_movies['Age'].replace({'16+': '16'}, inplace = True)
df_movies['Age'].replace({'18+': '18'}, inplace = True)
# df_movies['Age'] = df_movies['Age'].astype(int)
# IMDb
# df_movies.fillna({'IMDb' : df_movies['IMDb'].mean()}, inplace = True)
# df_movies.fillna({'IMDb' : df_movies['IMDb'].median()}, inplace = True)
df_movies.fillna({'IMDb' : "NA"}, inplace = True)
# Rotten Tomatoes
df_movies['Rotten Tomatoes'] = df_movies['Rotten Tomatoes'][df_movies['Rotten Tomatoes'].notnull()].str.replace('%', '').astype(int)
# df_movies['Rotten Tomatoes'] = df_movies['Rotten Tomatoes'][df_movies['Rotten Tomatoes'].notnull()].astype(int)
# df_movies.fillna({'Rotten Tomatoes' : df_movies['Rotten Tomatoes'].mean()}, inplace = True)
# df_movies.fillna({'Rotten Tomatoes' : df_movies['Rotten Tomatoes'].median()}, inplace = True)
# df_movies['Rotten Tomatoes'] = df_movies['Rotten Tomatoes'].astype(int)
df_movies.fillna({'Rotten Tomatoes' : "NA"}, inplace = True)
# Directors
# df_movies = df_movies.drop(['Directors'], axis = 1)
df_movies.fillna({'Directors' : "NA"}, inplace = True)
# Cast
df_movies.fillna({'Cast' : "NA"}, inplace = True)
# Genres
df_movies.fillna({'Genres': "NA"}, inplace = True)
# Country
df_movies.fillna({'Country': "NA"}, inplace = True)
# Language
df_movies.fillna({'Language': "NA"}, inplace = True)
# Plotline
df_movies.fillna({'Plotline': "NA"}, inplace = True)
# Runtime
# df_movies.fillna({'Runtime' : df_movies['Runtime'].mean()}, inplace = True)
# df_movies['Runtime'] = df_movies['Runtime'].astype(int)
df_movies.fillna({'Runtime' : "NA"}, inplace = True)
# Kind
# df_movies.fillna({'Kind': "NA"}, inplace = True)
# Type
# df_movies.fillna({'Type': "NA"}, inplace = True)
# df_movies = df_movies.drop(['Type'], axis = 1)
# Seasons
# df_movies.fillna({'Seasons': 1}, inplace = True)
# df_movies.fillna({'Seasons': "NA"}, inplace = True)
df_movies = df_movies.drop(['Seasons'], axis = 1)
# df_movies['Seasons'] = df_movies['Seasons'].astype(int)
# df_movies.fillna({'Seasons' : df_movies['Seasons'].mean()}, inplace = True)
# df_movies['Seasons'] = df_movies['Seasons'].astype(int)
# Service Provider
df_movies['Service Provider'] = df_movies.loc[:, ['Netflix', 'Prime Video', 'Disney+', 'Hulu']].idxmax(axis = 1)
# df_movies.drop(['Netflix','Prime Video','Disney+','Hulu'], axis = 1)
# Removing Duplicate and Missing Entries
df_movies.dropna(how = 'any', inplace = True)
df_movies.drop_duplicates(inplace = True)
data_investigate(df_movies)
No of Rows : 16923
No of Coloums : 20
**************************************************
Colums Names :
Index(['ID', 'Title', 'Year', 'Age', 'IMDb', 'Rotten Tomatoes', 'Directors',
'Cast', 'Genres', 'Country', 'Language', 'Plotline', 'Runtime', 'Kind',
'Netflix', 'Hulu', 'Prime Video', 'Disney+', 'Type',
'Service Provider'],
dtype='object')
**************************************************
Datatype of Columns :
ID int64
Title object
Year int64
Age object
IMDb object
Rotten Tomatoes object
Directors object
Cast object
Genres object
Country object
Language object
Plotline object
Runtime object
Kind object
Netflix int64
Hulu int64
Prime Video int64
Disney+ int64
Type int64
Service Provider object
dtype: object
**************************************************
Missing Values :
Series([], dtype: int64)
**************************************************
Missing vaules %age wise :
ID 0.0
Title 0.0
Year 0.0
Age 0.0
IMDb 0.0
Rotten Tomatoes 0.0
Directors 0.0
Cast 0.0
Genres 0.0
Country 0.0
Language 0.0
Plotline 0.0
Runtime 0.0
Kind 0.0
Netflix 0.0
Hulu 0.0
Prime Video 0.0
Disney+ 0.0
Type 0.0
Service Provider 0.0
dtype: float64
**************************************************
Pictorial Representation :
df_movies.head()
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | Language | Plotline | Runtime | Kind | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Inception | 2010 | 13 | 8.8 | 87 | Christopher Nolan | Leonardo DiCaprio,Joseph Gordon-Levitt,Elliot ... | Action,Adventure,Sci-Fi,Thriller | United States,United Kingdom | English,Japanese,French | Dom Cobb is a skilled thief, the absolute best... | 148 | movie | 1 | 0 | 0 | 0 | 0 | Netflix |
| 1 | 2 | The Matrix | 1999 | 16 | 8.7 | 88 | Lana Wachowski,Lilly Wachowski | Keanu Reeves,Laurence Fishburne,Carrie-Anne Mo... | Action,Sci-Fi | United States | English | Thomas A. Anderson is a man living two lives. ... | 136 | movie | 1 | 0 | 0 | 0 | 0 | Netflix |
| 2 | 3 | Avengers: Infinity War | 2018 | 13 | 8.4 | 85 | Anthony Russo,Joe Russo | Robert Downey Jr.,Chris Hemsworth,Mark Ruffalo... | Action,Adventure,Sci-Fi | United States | English | As the Avengers and their allies have continue... | 149 | movie | 1 | 0 | 0 | 0 | 0 | Netflix |
| 3 | 4 | Back to the Future | 1985 | 7 | 8.5 | 96 | Robert Zemeckis | Michael J. Fox,Christopher Lloyd,Lea Thompson,... | Adventure,Comedy,Sci-Fi | United States | English | Marty McFly, a typical American teenager of th... | 116 | movie | 1 | 0 | 0 | 0 | 0 | Netflix |
| 4 | 5 | The Good, the Bad and the Ugly | 1966 | 16 | 8.8 | 97 | Sergio Leone | Eli Wallach,Clint Eastwood,Lee Van Cleef,Aldo ... | Western | Italy,Spain,West Germany,United States | Italian | Blondie (The Good) (Clint Eastwood) is a profe... | 161 | movie | 1 | 0 | 1 | 0 | 0 | Netflix |
df_movies.describe()
| ID | Year | Netflix | Hulu | Prime Video | Disney+ | Type | |
|---|---|---|---|---|---|---|---|
| count | 16923.000000 | 16923.000000 | 16923.000000 | 16923.000000 | 16923.000000 | 16923.000000 | 16923.0 |
| mean | 8462.000000 | 2003.211901 | 0.214915 | 0.062637 | 0.727235 | 0.033150 | 0.0 |
| std | 4885.393638 | 20.526532 | 0.410775 | 0.242315 | 0.445394 | 0.179034 | 0.0 |
| min | 1.000000 | 1901.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 |
| 25% | 4231.500000 | 2001.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 |
| 50% | 8462.000000 | 2012.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 0.0 |
| 75% | 12692.500000 | 2016.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 0.0 |
| max | 16923.000000 | 2020.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 0.0 |
df_movies.corr()
| ID | Year | Netflix | Hulu | Prime Video | Disney+ | Type | |
|---|---|---|---|---|---|---|---|
| ID | 1.000000 | -0.217816 | -0.644470 | -0.129926 | 0.469301 | 0.263530 | NaN |
| Year | -0.217816 | 1.000000 | 0.256151 | 0.101337 | -0.255578 | -0.047258 | NaN |
| Netflix | -0.644470 | 0.256151 | 1.000000 | -0.118032 | -0.745141 | -0.089649 | NaN |
| Hulu | -0.129926 | 0.101337 | -0.118032 | 1.000000 | -0.284654 | -0.039693 | NaN |
| Prime Video | 0.469301 | -0.255578 | -0.745141 | -0.284654 | 1.000000 | -0.289008 | NaN |
| Disney+ | 0.263530 | -0.047258 | -0.089649 | -0.039693 | -0.289008 | 1.000000 | NaN |
| Type | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
# df_movies.sort_values('Year', ascending = True)
# df_movies.sort_values('IMDb', ascending = False)
# df_movies.to_csv(path_or_buf= '/content/drive/MyDrive/Files/updated_ottmovies.csv', index = False)
# path = '/content/drive/MyDrive/Files/'
# udf_movies = pd.read_csv(path + 'updated_ottmovies.csv')
# udf_movies
# df_netflix_movies = df_movies.loc[(df_movies['Netflix'] > 0)]
# df_hulu_movies = df_movies.loc[(df_movies['Hulu'] > 0)]
# df_prime_video_movies = df_movies.loc[(df_movies['Prime Video'] > 0)]
# df_disney_movies = df_movies.loc[(df_movies['Disney+'] > 0)]
df_netflix_only_movies = df_movies[(df_movies['Netflix'] == 1) & (df_movies['Hulu'] == 0) & (df_movies['Prime Video'] == 0 ) & (df_movies['Disney+'] == 0)]
df_hulu_only_movies = df_movies[(df_movies['Netflix'] == 0) & (df_movies['Hulu'] == 1) & (df_movies['Prime Video'] == 0 ) & (df_movies['Disney+'] == 0)]
df_prime_video_only_movies = df_movies[(df_movies['Netflix'] == 0) & (df_movies['Hulu'] == 0) & (df_movies['Prime Video'] == 1 ) & (df_movies['Disney+'] == 0)]
df_disney_only_movies = df_movies[(df_movies['Netflix'] == 0) & (df_movies['Hulu'] == 0) & (df_movies['Prime Video'] == 0 ) & (df_movies['Disney+'] == 1)]
df_movies_directors = df_movies.copy()
df_movies_directors.drop(df_movies_directors.loc[df_movies_directors['Directors'] == "NA"].index, inplace = True)
# df_movies_directors = df_movies_directors[df_movies_directors.Director != "NA"]
# df_movies_directors['Director'] = df_movies_directors['Director'].astype(str)
df_movies_count_directors = df_movies_directors.copy()
df_movies_director = df_movies_directors.copy()
# Create directors dict where key=name and value = number of directors
directors = {}
for i in df_movies_count_directors['Directors'].dropna():
if i != "NA":
#print(i,len(i.split(',')))
directors[i] = len(i.split(','))
else:
directors[i] = 0
# Add this information to our dataframe as a new column
df_movies_count_directors['Number of Directors'] = df_movies_count_directors['Directors'].map(directors).astype(int)
df_movies_mixed_directors = df_movies_count_directors.copy()
# Creating distinct dataframes only with the movies present on individual streaming platforms
netflix_directors_movies = df_movies_count_directors.loc[df_movies_count_directors['Netflix'] == 1]
hulu_directors_movies = df_movies_count_directors.loc[df_movies_count_directors['Hulu'] == 1]
prime_video_directors_movies = df_movies_count_directors.loc[df_movies_count_directors['Prime Video'] == 1]
disney_directors_movies = df_movies_count_directors.loc[df_movies_count_directors['Disney+'] == 1]
plt.figure(figsize = (10, 10))
corr = df_movies_count_directors.corr()
# Plot figsize
fig, ax = plt.subplots(figsize=(10, 8))
# Generate Heat Map, alleast annotations and place floats in map
sns.heatmap(corr, cmap = 'magma', annot = True, fmt = ".2f")
# Apply xticks
plt.xticks(range(len(corr.columns)), corr.columns);
# Apply yticks
plt.yticks(range(len(corr.columns)), corr.columns)
# show plot
plt.show()
fig.show()
<Figure size 720x720 with 0 Axes>
df_directors_most_movies = df_movies_count_directors.sort_values(by = 'Number of Directors', ascending = False).reset_index()
df_directors_most_movies = df_directors_most_movies.drop(['index'], axis = 1)
# filter = (df_movies_count_directors['Number of Directors'] == (df_movies_count_directors['Number of Directors'].max()))
# df_directors_most_movies = df_movies_count_directors[filter]
# mostest_rated_movies = df_movies_count_directors.loc[df_movies_count_directors['Number of Directors'].idxmax()]
print('\nMovies with Highest Ever Number of Directors are : \n')
df_directors_most_movies.head(5)
Movies with Highest Ever Number of Directors are :
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | ... | Plotline | Runtime | Kind | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | Number of Directors | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 11086 | The Owner | 2012 | NR | 6.9 | NA | Xavier Agudo,Ian Bonner,Michael Canzoniero,Fra... | Jorge Mario Agudelo,Chiraz Aich,Christine Altm... | Drama | United States,Germany | ... | NA | 94 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video | 28 |
| 1 | 12269 | Fun Size Horror: Volume One | 2015 | NR | 4.6 | NA | Bryan Chojnowski,Lisa J Dooley,Ned Ehrbar,Mali... | Tara Perry,Aidan Flynn,Guy Perry,Nev Scharrel,... | Horror | United States | ... | Alienated by her peers as a young girl, Scarle... | 86 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video | 18 |
| 2 | 11869 | A Taste of Phobia | 2018 | NR | 3.1 | NA | Domiziano Cristopharo,Jason Impey,Sunny King,S... | Lianne O'Shea,Kehinde Bankole,Roberta Gemma,Ma... | Horror | United Kingdom | ... | Blending drama with the explanations of passio... | 90 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video | 17 |
| 3 | 4422 | The Proposition | 2005 | 16 | 4.3 | 85 | Elizabeth Banks,Steven Brill,Steve Carr,Rusty ... | Dennis Quaid,Greg Kinnear,Common,Charlie Saxto... | Comedy | United States | ... | Ineffectual, 'has-been' film-maker (Dennis Qua... | 94 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video | 13 |
| 4 | 2267 | Truth or Dare | 2017 | 16 | 4.3 | NA | Elizabeth Banks,Steven Brill,Steve Carr,Rusty ... | Dennis Quaid,Greg Kinnear,Common,Charlie Saxto... | Comedy | United States | ... | Ineffectual, 'has-been' film-maker (Dennis Qua... | 94 | movie | 1 | 0 | 1 | 0 | 0 | Netflix | 13 |
5 rows × 21 columns
fig = px.bar(y = df_directors_most_movies['Title'][:15],
x = df_directors_most_movies['Number of Directors'][:15],
color = df_directors_most_movies['Number of Directors'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies', 'x' : 'Number of Directors'},
title = 'Movies with Highest Number of Directors : All Platforms')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
df_directors_least_movies = df_movies_count_directors.sort_values(by = 'Number of Directors', ascending = True).reset_index()
df_directors_least_movies = df_directors_least_movies.drop(['index'], axis = 1)
# filter = (df_movies_count_directors['Number of Directors'] == (df_movies_count_directors['Number of Directors'].min()))
# df_directors_least_movies = df_movies_count_directors[filter]
print('\nMovies with Lowest Ever Number of Directors are : \n')
df_directors_least_movies.head(5)
Movies with Lowest Ever Number of Directors are :
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | ... | Plotline | Runtime | Kind | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | Number of Directors | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Inception | 2010 | 13 | 8.8 | 87 | Christopher Nolan | Leonardo DiCaprio,Joseph Gordon-Levitt,Elliot ... | Action,Adventure,Sci-Fi,Thriller | United States,United Kingdom | ... | Dom Cobb is a skilled thief, the absolute best... | 148 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | 1 |
| 1 | 10899 | Secret Mission | 1942 | NR | 5.5 | NA | Harold French | Hugh Williams,Carla Lehmann,James Mason,Roland... | Drama,Thriller,War | United Kingdom | ... | 40 Nights is the first of the QUEST TRILOGY - ... | 94 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video | 1 |
| 2 | 10901 | Her Side of the Bed | 2018 | NR | 4.1 | NA | Bryn Woznicki | Chelsea Morgan,Bryn Woznicki,Kissyc Alonso,Ada... | Comedy,Drama,Romance | United States | ... | 'The Count of Monte Cristo' is an adaptation o... | 97 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video | 1 |
| 3 | 10902 | Kyle Cease: Weirder. Blacker. Dimpler. | 2007 | NR | 7 | NA | Craig Kelly | Kyle Cease | Comedy | United States | ... | In Barefoot County, hot mama Mary Ann Hogan ru... | 60 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video | 1 |
| 4 | 10903 | My Life Without Air | 2017 | 13 | 7.1 | NA | Bojana Burnac | Goran Colak,Ivan Drvis | Documentary | Croatia | ... | Handguns figure in the intertwining lives of n... | 72 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video | 1 |
5 rows × 21 columns
fig = px.bar(y = df_directors_least_movies['Title'][:15],
x = df_directors_least_movies['Number of Directors'][:15],
color = df_directors_least_movies['Number of Directors'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies', 'x' : 'Number of Directors'},
title = 'Movies with Lowest Number of Directors : All Platforms')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
print(f'''
Total '{df_movies_count_directors['Number of Directors'].unique().shape[0]}' unique Number of Directors s were Given, They were Like this,\n
{df_movies_count_directors.sort_values(by = 'Number of Directors', ascending = False)['Number of Directors'].unique()}\n
The Highest Number of Directors Ever Any Movie Got is '{df_directors_most_movies['Title'][0]}' : '{df_directors_most_movies['Number of Directors'].max()}'\n
The Lowest Number of Directors Ever Any Movie Got is '{df_directors_least_movies['Title'][0]}' : '{df_directors_least_movies['Number of Directors'].min()}'\n
''')
Total '16' unique Number of Directors s were Given, They were Like this,
[28 18 17 13 12 11 10 9 8 7 6 5 4 3 2 1]
The Highest Number of Directors Ever Any Movie Got is 'The Owner' : '28'
The Lowest Number of Directors Ever Any Movie Got is 'Inception' : '1'
netflix_directors_most_movies = df_directors_most_movies.loc[df_directors_most_movies['Netflix']==1].reset_index()
netflix_directors_most_movies = netflix_directors_most_movies.drop(['index'], axis = 1)
netflix_directors_least_movies = df_directors_least_movies.loc[df_directors_least_movies['Netflix']==1].reset_index()
netflix_directors_least_movies = netflix_directors_least_movies.drop(['index'], axis = 1)
netflix_directors_most_movies.head(5)
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | ... | Plotline | Runtime | Kind | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | Number of Directors | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2267 | Truth or Dare | 2017 | 16 | 4.3 | NA | Elizabeth Banks,Steven Brill,Steve Carr,Rusty ... | Dennis Quaid,Greg Kinnear,Common,Charlie Saxto... | Comedy | United States | ... | Ineffectual, 'has-been' film-maker (Dennis Qua... | 94 | movie | 1 | 0 | 1 | 0 | 0 | Netflix | 13 |
| 1 | 359 | Veronica | 2017 | 16 | 4.3 | 79 | Elizabeth Banks,Steven Brill,Steve Carr,Rusty ... | Dennis Quaid,Greg Kinnear,Common,Charlie Saxto... | Comedy | United States | ... | Ineffectual, 'has-been' film-maker (Dennis Qua... | 94 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | 13 |
| 2 | 2358 | Berlin, I Love You | 2019 | 16 | 4.6 | 11 | Dianna Agron,Peter Chelsom,Claus Clausen,Ferna... | Keira Knightley,Helen Mirren,Luke Wilson,Jim S... | Drama,Romance | Germany | ... | NA | 120 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | 13 |
| 3 | 2974 | X: Past Is Present | 2015 | 13 | 5.3 | NA | Hemant Gaba,Pratim D. Gupta,Sudhish Kamath,Nal... | Rajat Kapoor,Radhika Apte,Bidita Bag,Piaa Bajp... | Drama,Mystery,Romance | India | ... | NA | 105 | movie | 1 | 0 | 1 | 0 | 0 | Netflix | 11 |
| 4 | 660 | Kahlil Gibran's The Prophet | 2014 | 7 | 7.1 | 66 | Roger Allers,Gaëtan Brizzi,Paul Brizzi,Joan C.... | Liam Neeson,Salma Hayek,John Krasinski,Frank L... | Animation,Drama | Qatar,France,Lebanon,Canada,United States,Irel... | ... | NA | 85 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | 10 |
5 rows × 21 columns
fig = px.bar(y = netflix_directors_most_movies['Title'][:15],
x = netflix_directors_most_movies['Number of Directors'][:15],
color = netflix_directors_most_movies['Number of Directors'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies', 'x' : 'Number of Directors'},
title = 'Movies with Highest Number of Directors : Netflix')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
fig = px.bar(y = netflix_directors_least_movies['Title'][:15],
x = netflix_directors_least_movies['Number of Directors'][:15],
color = netflix_directors_least_movies['Number of Directors'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies', 'x' : 'Number of Directors'},
title = 'Movies with Lowest Number of Directors : Netflix')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
hulu_directors_most_movies = df_directors_most_movies.loc[df_directors_most_movies['Hulu']==1].reset_index()
hulu_directors_most_movies = hulu_directors_most_movies.drop(['index'], axis = 1)
hulu_directors_least_movies = df_directors_least_movies.loc[df_directors_least_movies['Hulu']==1].reset_index()
hulu_directors_least_movies = hulu_directors_least_movies.drop(['index'], axis = 1)
hulu_directors_most_movies.head(5)
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | ... | Plotline | Runtime | Kind | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | Number of Directors | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 4032 | Wakko's Wish | 1999 | 0 | 7.3 | NA | Liz Holzman,Rusty Mills,Tom Ruegger,Russell Ca... | Rob Paulsen,Jess Harnell,Tress MacNeille,Mauri... | Animation,Adventure,Comedy,Drama,Family,Fantas... | United States | ... | The Warner Brothers (and the Warner Sister) go... | 80 | movie | 0 | 1 | 0 | 0 | 0 | Hulu | 8 |
| 1 | 3704 | Southbound | 2015 | 16 | 5.9 | 81 | Roxanne Benjamin,Matt Bettinelli-Olpin,David B... | Chad Villella,Matt Bettinelli-Olpin,Kristina P... | Horror | United States | ... | On a desolate stretch of desert highway, weary... | 89 | movie | 0 | 1 | 1 | 0 | 0 | Prime Video | 8 |
| 2 | 3912 | Tiny Toon Adventures: How I Spent My Vacation | 1992 | NR | 8 | NA | Rich Arons,Ken Boyer,Kent Butterworth,Barry Ca... | Charlie Adler,Tress MacNeille,Joe Alaskey,Don ... | Animation,Adventure,Comedy,Family,Fantasy | United States | ... | Term-time ends at Acme Looniversity and the Ti... | 79 | movie | 0 | 1 | 0 | 0 | 0 | Hulu | 7 |
| 3 | 16501 | Victoria's Secret Fashion Show | 1999 | 7 | 7.6 | NA | Hamish Hamilton,Yemisi Brookes,Dee Koppang O'L... | Behati Prinsloo,Adriana Lima,Alessandra Ambros... | Reality-TV | United States | ... | Shinichi Kanou is a young secluded Otaku who i... | 45 | movie | 0 | 1 | 0 | 0 | 0 | Hulu | 4 |
| 4 | 3531 | The Prince of Egypt | 1998 | 7 | 7.1 | 80 | Brenda Chapman,Steve Hickner,Simon Wells | Val Kilmer,Ralph Fiennes,Michelle Pfeiffer,San... | Animation,Adventure,Drama,Family,Fantasy,Musical | United States,France,United Kingdom | ... | This is the extraordinary tale of two brothers... | 99 | movie | 0 | 1 | 0 | 0 | 0 | Hulu | 3 |
5 rows × 21 columns
fig = px.bar(y = hulu_directors_most_movies['Title'][:15],
x = hulu_directors_most_movies['Number of Directors'][:15],
color = hulu_directors_most_movies['Number of Directors'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies', 'x' : 'Number of Directors'},
title = 'Movies with Highest Number of Directors : Hulu')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
fig = px.bar(y = hulu_directors_least_movies['Title'][:15],
x = hulu_directors_least_movies['Number of Directors'][:15],
color = hulu_directors_least_movies['Number of Directors'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies', 'x' : 'Number of Directors'},
title = 'Movies with Lowest Number of Directors : Hulu')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
prime_video_directors_most_movies = df_directors_most_movies.loc[df_directors_most_movies['Prime Video']==1].reset_index()
prime_video_directors_most_movies = prime_video_directors_most_movies.drop(['index'], axis = 1)
prime_video_directors_least_movies = df_directors_least_movies.loc[df_directors_least_movies['Prime Video']==1].reset_index()
prime_video_directors_least_movies = prime_video_directors_least_movies.drop(['index'], axis = 1)
prime_video_directors_most_movies.head(5)
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | ... | Plotline | Runtime | Kind | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | Number of Directors | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 11086 | The Owner | 2012 | NR | 6.9 | NA | Xavier Agudo,Ian Bonner,Michael Canzoniero,Fra... | Jorge Mario Agudelo,Chiraz Aich,Christine Altm... | Drama | United States,Germany | ... | NA | 94 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video | 28 |
| 1 | 12269 | Fun Size Horror: Volume One | 2015 | NR | 4.6 | NA | Bryan Chojnowski,Lisa J Dooley,Ned Ehrbar,Mali... | Tara Perry,Aidan Flynn,Guy Perry,Nev Scharrel,... | Horror | United States | ... | Alienated by her peers as a young girl, Scarle... | 86 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video | 18 |
| 2 | 11869 | A Taste of Phobia | 2018 | NR | 3.1 | NA | Domiziano Cristopharo,Jason Impey,Sunny King,S... | Lianne O'Shea,Kehinde Bankole,Roberta Gemma,Ma... | Horror | United Kingdom | ... | Blending drama with the explanations of passio... | 90 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video | 17 |
| 3 | 4422 | The Proposition | 2005 | 16 | 4.3 | 85 | Elizabeth Banks,Steven Brill,Steve Carr,Rusty ... | Dennis Quaid,Greg Kinnear,Common,Charlie Saxto... | Comedy | United States | ... | Ineffectual, 'has-been' film-maker (Dennis Qua... | 94 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video | 13 |
| 4 | 2267 | Truth or Dare | 2017 | 16 | 4.3 | NA | Elizabeth Banks,Steven Brill,Steve Carr,Rusty ... | Dennis Quaid,Greg Kinnear,Common,Charlie Saxto... | Comedy | United States | ... | Ineffectual, 'has-been' film-maker (Dennis Qua... | 94 | movie | 1 | 0 | 1 | 0 | 0 | Netflix | 13 |
5 rows × 21 columns
fig = px.bar(y = prime_video_directors_most_movies['Title'][:15],
x = prime_video_directors_most_movies['Number of Directors'][:15],
color = prime_video_directors_most_movies['Number of Directors'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies', 'x' : 'Number of Directors'},
title = 'Movies with Highest Number of Directors : Prime Video')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
fig = px.bar(y = prime_video_directors_least_movies['Title'][:15],
x = prime_video_directors_least_movies['Number of Directors'][:15],
color = prime_video_directors_least_movies['Number of Directors'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies', 'x' : 'Number of Directors'},
title = 'Movies with Lowest Number of Directors : Prime Video')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
disney_directors_most_movies = df_directors_most_movies.loc[df_directors_most_movies['Disney+']==1].reset_index()
disney_directors_most_movies = disney_directors_most_movies.drop(['index'], axis = 1)
disney_directors_least_movies = df_directors_least_movies.loc[df_directors_least_movies['Disney+']==1].reset_index()
disney_directors_least_movies = disney_directors_least_movies.drop(['index'], axis = 1)
disney_directors_most_movies.head(5)
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | ... | Plotline | Runtime | Kind | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | Number of Directors | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 15786 | Fantasia | 1940 | 0 | 7.7 | 95 | James Algar,Samuel Armstrong,Ford Beebe Jr.,No... | Deems Taylor,Leopold Stokowski,The Philadelphi... | Animation,Family,Fantasy,Music,Musical | United States | ... | Alice, an unpretentious and individual 19-year... | 125 | movie | 0 | 0 | 0 | 1 | 0 | Disney+ | 12 |
| 1 | 15797 | Bambi | 1942 | 0 | 7.3 | 90 | James Algar,Samuel Armstrong,David Hand,Graham... | Hardie Albright,Stan Alexander,Bobette Audrey,... | Animation,Drama,Family | United States | ... | When two pre-teens named Hallie and Annie meet... | 70 | movie | 0 | 0 | 0 | 1 | 0 | Disney+ | 9 |
| 2 | 16077 | Belle's Magical World | 1998 | 0 | 5.3 | 17 | Bob Kline,Cullen Blaine,Dale Case,Daniel de la... | Jeff Bennett,Robby Benson,Paige O'Hara,Jim Cum... | Animation,Comedy,Family,Fantasy,Musical,Romance | United States | ... | Robbie, the master's baby, has been mysterious... | 92 | movie | 0 | 0 | 0 | 1 | 0 | Disney+ | 8 |
| 3 | 15835 | Fantasia 2000 | 1999 | 0 | 7.2 | 81 | James Algar,Gaëtan Brizzi,Paul Brizzi,Hendel B... | Steve Martin,Itzhak Perlman,Quincy Jones,Bette... | Animation,Comedy,Family,Fantasy,Music | United States | ... | Captain Jack Sparrow (Johnny Depp) crosses pat... | 75 | movie | 0 | 0 | 0 | 1 | 0 | Disney+ | 8 |
| 4 | 15777 | Snow White and the Seven Dwarfs | 1937 | 0 | 7.6 | 98 | William Cottrell,David Hand,Wilfred Jackson,La... | Roy Atwell,Stuart Buchanan,Adriana Caselotti,E... | Animation,Family,Fantasy,Musical,Romance | United States | ... | NA | 83 | movie | 0 | 0 | 0 | 1 | 0 | Disney+ | 6 |
5 rows × 21 columns
fig = px.bar(y = disney_directors_most_movies['Title'][:15],
x = disney_directors_most_movies['Number of Directors'][:15],
color = disney_directors_most_movies['Number of Directors'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies', 'x' : 'Number of Directors'},
title = 'Movies with Highest Number of Directors : Disney+')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
fig = px.bar(y = disney_directors_least_movies['Title'][:15],
x = disney_directors_least_movies['Number of Directors'][:15],
color = disney_directors_least_movies['Number of Directors'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies', 'x' : 'Number of Directors'},
title = 'Movies with Lowest Number of Directors : Disney+')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
print(f'''
The Movie with Highest Number of Directors Ever Got is '{df_directors_most_movies['Title'][0]}' : '{df_directors_most_movies['Number of Directors'].max()}'\n
The Movie with Lowest Number of Directors Ever Got is '{df_directors_least_movies['Title'][0]}' : '{df_directors_least_movies['Number of Directors'].min()}'\n
The Movie with Highest Number of Directors on 'Netflix' is '{netflix_directors_most_movies['Title'][0]}' : '{netflix_directors_most_movies['Number of Directors'].max()}'\n
The Movie with Lowest Number of Directors on 'Netflix' is '{netflix_directors_least_movies['Title'][0]}' : '{netflix_directors_least_movies['Number of Directors'].min()}'\n
The Movie with Highest Number of Directors on 'Hulu' is '{hulu_directors_most_movies['Title'][0]}' : '{hulu_directors_most_movies['Number of Directors'].max()}'\n
The Movie with Lowest Number of Directors on 'Hulu' is '{hulu_directors_least_movies['Title'][0]}' : '{hulu_directors_least_movies['Number of Directors'].min()}'\n
The Movie with Highest Number of Directors on 'Prime Video' is '{prime_video_directors_most_movies['Title'][0]}' : '{prime_video_directors_most_movies['Number of Directors'].max()}'\n
The Movie with Lowest Number of Directors on 'Prime Video' is '{prime_video_directors_least_movies['Title'][0]}' : '{prime_video_directors_least_movies['Number of Directors'].min()}'\n
The Movie with Highest Number of Directors on 'Disney+' is '{disney_directors_most_movies['Title'][0]}' : '{disney_directors_most_movies['Number of Directors'].max()}'\n
The Movie with Lowest Number of Directors on 'Disney+' is '{disney_directors_least_movies['Title'][0]}' : '{disney_directors_least_movies['Number of Directors'].min()}'\n
''')
The Movie with Highest Number of Directors Ever Got is 'The Owner' : '28'
The Movie with Lowest Number of Directors Ever Got is 'Inception' : '1'
The Movie with Highest Number of Directors on 'Netflix' is 'Truth or Dare' : '13'
The Movie with Lowest Number of Directors on 'Netflix' is 'Inception' : '1'
The Movie with Highest Number of Directors on 'Hulu' is 'Wakko's Wish' : '8'
The Movie with Lowest Number of Directors on 'Hulu' is 'Home Free' : '1'
The Movie with Highest Number of Directors on 'Prime Video' is 'The Owner' : '28'
The Movie with Lowest Number of Directors on 'Prime Video' is 'Secret Mission' : '1'
The Movie with Highest Number of Directors on 'Disney+' is 'Fantasia' : '12'
The Movie with Lowest Number of Directors on 'Disney+' is 'The Swap' : '1'
print(f'''
Accross All Platforms the Average Number of Directors is '{round(df_movies_count_directors['Number of Directors'].mean(), ndigits = 2)}'\n
The Average Number of Directors on 'Netflix' is '{round(netflix_directors_movies['Number of Directors'].mean(), ndigits = 2)}'\n
The Average Number of Directors on 'Hulu' is '{round(hulu_directors_movies['Number of Directors'].mean(), ndigits = 2)}'\n
The Average Number of Directors on 'Prime Video' is '{round(prime_video_directors_movies['Number of Directors'].mean(), ndigits = 2)}'\n
The Average Number of Directors on 'Disney+' is '{round(disney_directors_movies['Number of Directors'].mean(), ndigits = 2)}'\n
''')
Accross All Platforms the Average Number of Directors is '1.14'
The Average Number of Directors on 'Netflix' is '1.15'
The Average Number of Directors on 'Hulu' is '1.12'
The Average Number of Directors on 'Prime Video' is '1.13'
The Average Number of Directors on 'Disney+' is '1.34'
print(f'''
Accross All Platforms Total Count of Director is '{df_movies_count_directors['Number of Directors'].max()}'\n
Total Count of Director on 'Netflix' is '{netflix_directors_movies['Number of Directors'].max()}'\n
Total Count of Director on 'Hulu' is '{hulu_directors_movies['Number of Directors'].max()}'\n
Total Count of Director on 'Prime Video' is '{prime_video_directors_movies['Number of Directors'].max()}'\n
Total Count of Director on 'Disney+' is '{disney_directors_movies['Number of Directors'].max()}'\n
''')
Accross All Platforms Total Count of Director is '28'
Total Count of Director on 'Netflix' is '13'
Total Count of Director on 'Hulu' is '8'
Total Count of Director on 'Prime Video' is '28'
Total Count of Director on 'Disney+' is '12'
f, ax = plt.subplots(1, 2 , figsize = (20, 5))
sns.distplot(df_movies_count_directors['Number of Directors'],bins = 20, kde = True, ax = ax[0])
sns.boxplot(df_movies_count_directors['Number of Directors'], ax = ax[1])
plt.show()
# Defining plot size and title
plt.figure(figsize = (20, 5))
plt.title('Number of Directors s Per Platform')
# Plotting the information from each dataset into a histogram
sns.histplot(prime_video_directors_movies['Number of Directors'], color = 'lightblue', legend = True, kde = True)
sns.histplot(netflix_directors_movies['Number of Directors'], color = 'red', legend = True, kde = True)
sns.histplot(hulu_directors_movies['Number of Directors'], color = 'lightgreen', legend = True, kde = True)
sns.histplot(disney_directors_movies['Number of Directors'], color = 'darkblue', legend = True, kde = True)
# Setting the legend
plt.legend(['Prime Video', 'Netflix', 'Hulu', 'Disney+'])
plt.show()
df_lan = df_movies_director['Directors'].str.split(',').apply(pd.Series).stack()
del df_movies_director['Directors']
df_lan.index = df_lan.index.droplevel(-1)
df_lan.name = 'Director'
df_movies_director = df_movies_director.join(df_lan)
df_movies_director.drop_duplicates(inplace = True)
df_movies_director.head(5)
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Cast | Genres | Country | Language | Plotline | Runtime | Kind | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | Director | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Inception | 2010 | 13 | 8.8 | 87 | Leonardo DiCaprio,Joseph Gordon-Levitt,Elliot ... | Action,Adventure,Sci-Fi,Thriller | United States,United Kingdom | English,Japanese,French | Dom Cobb is a skilled thief, the absolute best... | 148 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | Christopher Nolan |
| 1 | 2 | The Matrix | 1999 | 16 | 8.7 | 88 | Keanu Reeves,Laurence Fishburne,Carrie-Anne Mo... | Action,Sci-Fi | United States | English | Thomas A. Anderson is a man living two lives. ... | 136 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | Lana Wachowski |
| 1 | 2 | The Matrix | 1999 | 16 | 8.7 | 88 | Keanu Reeves,Laurence Fishburne,Carrie-Anne Mo... | Action,Sci-Fi | United States | English | Thomas A. Anderson is a man living two lives. ... | 136 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | Lilly Wachowski |
| 2 | 3 | Avengers: Infinity War | 2018 | 13 | 8.4 | 85 | Robert Downey Jr.,Chris Hemsworth,Mark Ruffalo... | Action,Adventure,Sci-Fi | United States | English | As the Avengers and their allies have continue... | 149 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | Anthony Russo |
| 2 | 3 | Avengers: Infinity War | 2018 | 13 | 8.4 | 85 | Robert Downey Jr.,Chris Hemsworth,Mark Ruffalo... | Action,Adventure,Sci-Fi | United States | English | As the Avengers and their allies have continue... | 149 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | Joe Russo |
director_count = df_movies_director.groupby('Director')['Title'].count()
director_movies = df_movies_director.groupby('Director')[['Netflix', 'Hulu', 'Prime Video', 'Disney+']].sum()
director_data_movies = pd.concat([director_count, director_movies], axis = 1).reset_index().rename(columns = {'Title' : 'Movies Count'})
director_data_movies = director_data_movies.sort_values(by = 'Movies Count', ascending = False)
# Director with Movies Counts - All Platforms Combined
director_data_movies.sort_values(by = 'Movies Count', ascending = False)[:10]
| Director | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 5226 | Jay Chapman | 36 | 12 | 0 | 29 | 0 |
| 6248 | Joseph Kane | 31 | 0 | 0 | 31 | 0 |
| 1985 | Cheh Chang | 29 | 2 | 0 | 28 | 0 |
| 10597 | Sam Newfield | 23 | 1 | 0 | 22 | 0 |
| 5621 | Jim Wynorski | 23 | 0 | 0 | 23 | 0 |
| 5107 | Jan Suter | 21 | 21 | 0 | 0 | 0 |
| 2792 | David DeCoteau | 21 | 0 | 0 | 21 | 0 |
| 9783 | Raúl Campos | 21 | 21 | 0 | 0 | 0 |
| 5232 | Jay Karas | 21 | 15 | 1 | 6 | 1 |
| 12460 | William Beaudine | 20 | 0 | 0 | 20 | 0 |
fig = px.bar(x = director_data_movies['Director'][:50],
y = director_data_movies['Movies Count'][:50],
color = director_data_movies['Movies Count'][:50],
color_continuous_scale = 'Teal_r',
labels = { 'x' : 'Director', 'y' : 'Movies Count'},
title = 'Major Directors : All Platforms')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
df_director_high_movies = director_data_movies.sort_values(by = 'Movies Count', ascending = False).reset_index()
df_director_high_movies = df_director_high_movies.drop(['index'], axis = 1)
# filter = (director_data_movies['Movies Count'] == (director_data_movies['Movies Count'].max()))
# df_director_high_movies = director_data_movies[filter]
# highest_rated_movies = director_data_movies.loc[director_data_movies['Movies Count'].idxmax()]
print('\nDirector with Highest Ever Movies Count are : All Platforms Combined\n')
df_director_high_movies.head(5)
Director with Highest Ever Movies Count are : All Platforms Combined
| Director | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | Jay Chapman | 36 | 12 | 0 | 29 | 0 |
| 1 | Joseph Kane | 31 | 0 | 0 | 31 | 0 |
| 2 | Cheh Chang | 29 | 2 | 0 | 28 | 0 |
| 3 | Sam Newfield | 23 | 1 | 0 | 22 | 0 |
| 4 | Jim Wynorski | 23 | 0 | 0 | 23 | 0 |
fig = px.bar(y = df_director_high_movies['Director'][:15],
x = df_director_high_movies['Movies Count'][:15],
color = df_director_high_movies['Movies Count'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Director', 'x' : 'Movies Count'},
title = 'Director with Highest Movies : All Platforms')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
df_director_low_movies = director_data_movies.sort_values(by = 'Movies Count', ascending = True).reset_index()
df_director_low_movies = df_director_low_movies.drop(['index'], axis = 1)
# filter = (director_data_movies['Movies Count'] == (director_data_movies['Movies Count'].min()))
# df_director_low_movies = director_data_movies[filter]
print('\nDirector with Lowest Ever Movies Count are : All Platforms Combined\n')
df_director_low_movies.head(5)
Director with Lowest Ever Movies Count are : All Platforms Combined
| Director | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | Peter Ettedgui | 1 | 0 | 0 | 1 | 0 |
| 1 | Dexton Deboree | 1 | 0 | 1 | 0 | 0 |
| 2 | Dezsö Magyar | 1 | 0 | 0 | 1 | 0 |
| 3 | Dhanush | 1 | 1 | 0 | 1 | 0 |
| 4 | Dheer Momaya | 1 | 1 | 0 | 0 | 0 |
fig = px.bar(y = df_director_low_movies['Director'][:15],
x = df_director_low_movies['Movies Count'][:15],
color = df_director_low_movies['Movies Count'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Director', 'x' : 'Movies Count'},
title = 'Director with Lowest Movies Count : All Platforms')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
print(f'''
Total '{director_data_movies['Director'].unique().shape[0]}' unique Director Count s were Given, They were Like this,\n
{director_data_movies.sort_values(by = 'Movies Count', ascending = False)['Director'].unique()[:5]}\n
The Highest Ever Movies Count Ever Any Movie Got is '{df_director_high_movies['Director'][0]}' : '{df_director_high_movies['Movies Count'].max()}'\n
The Lowest Ever Movies Count Ever Any Movie Got is '{df_director_low_movies['Director'][0]}' : '{df_director_low_movies['Movies Count'].min()}'\n
''')
Total '12760' unique Director Count s were Given, They were Like this,
['Jay Chapman' 'Joseph Kane' 'Cheh Chang' 'Sam Newfield' 'Jim Wynorski']
The Highest Ever Movies Count Ever Any Movie Got is 'Jay Chapman' : '36'
The Lowest Ever Movies Count Ever Any Movie Got is 'Peter Ettedgui' : '1'
fig = px.pie(director_data_movies[:10], names = 'Director', values = 'Movies Count', color_discrete_sequence = px.colors.sequential.Teal)
fig.update_traces(textposition = 'inside', textinfo = 'percent+label', title = 'Movies Count based on Director')
fig.show()
# netflix_director_movies = director_data_movies[director_data_movies['Netflix'] != 0].sort_values(by = 'Netflix', ascending = False).reset_index()
# netflix_director_movies = netflix_director_movies.drop(['index', 'Hulu', 'Prime Video', 'Disney+', 'Movies Count'], axis = 1)
netflix_director_high_movies = df_director_high_movies.sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_director_high_movies = netflix_director_high_movies.drop(['index'], axis = 1)
netflix_director_low_movies = df_director_high_movies.sort_values(by = 'Netflix', ascending = True).reset_index()
netflix_director_low_movies = netflix_director_low_movies.drop(['index'], axis = 1)
netflix_director_high_movies.head(5)
| Director | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | Jan Suter | 21 | 21 | 0 | 0 | 0 |
| 1 | Raúl Campos | 21 | 21 | 0 | 0 | 0 |
| 2 | Marcus Raboy | 18 | 16 | 0 | 2 | 0 |
| 3 | Jay Karas | 21 | 15 | 1 | 6 | 1 |
| 4 | Jay Chapman | 36 | 12 | 0 | 29 | 0 |
fig = px.bar(x = netflix_director_high_movies['Director'][:15],
y = netflix_director_high_movies['Netflix'][:15],
color = netflix_director_high_movies['Netflix'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Director', 'x' : 'Movies Count'},
title = 'Director with Highest Movies : Netflix')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
# hulu_director_movies = director_data_movies[director_data_movies['Hulu'] != 0].sort_values(by = 'Hulu', ascending = False).reset_index()
# hulu_director_movies = hulu_director_movies.drop(['index', 'Netflix', 'Prime Video', 'Disney+', 'Movies Count'], axis = 1)
hulu_director_high_movies = df_director_high_movies.sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_director_high_movies = hulu_director_high_movies.drop(['index'], axis = 1)
hulu_director_low_movies = df_director_high_movies.sort_values(by = 'Hulu', ascending = True).reset_index()
hulu_director_low_movies = hulu_director_low_movies.drop(['index'], axis = 1)
hulu_director_high_movies.head(5)
| Director | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | Savage Steve Holland | 6 | 2 | 5 | 0 | 1 |
| 1 | Tyler Perry | 6 | 2 | 4 | 4 | 0 |
| 2 | Richard Rich | 10 | 2 | 4 | 2 | 2 |
| 3 | Alan Metter | 4 | 1 | 3 | 1 | 0 |
| 4 | William Lau | 5 | 2 | 3 | 0 | 0 |
fig = px.bar(x = hulu_director_high_movies['Director'][:15],
y = hulu_director_high_movies['Hulu'][:15],
color = hulu_director_high_movies['Hulu'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Director', 'x' : 'Movies Count'},
title = 'Director with Highest Movies : Hulu')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
# prime_video_director_movies = director_data_movies[director_data_movies['Prime Video'] != 0].sort_values(by = 'Prime Video', ascending = False).reset_index()
# prime_video_director_movies = prime_video_director_movies.drop(['index', 'Netflix', 'Hulu', 'Disney+', 'Movies Count'], axis = 1)
prime_video_director_high_movies = df_director_high_movies.sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_director_high_movies = prime_video_director_high_movies.drop(['index'], axis = 1)
prime_video_director_low_movies = df_director_high_movies.sort_values(by = 'Prime Video', ascending = True).reset_index()
prime_video_director_low_movies = prime_video_director_low_movies.drop(['index'], axis = 1)
prime_video_director_high_movies.head(5)
| Director | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | Joseph Kane | 31 | 0 | 0 | 31 | 0 |
| 1 | Jay Chapman | 36 | 12 | 0 | 29 | 0 |
| 2 | Cheh Chang | 29 | 2 | 0 | 28 | 0 |
| 3 | Jim Wynorski | 23 | 0 | 0 | 23 | 0 |
| 4 | Sam Newfield | 23 | 1 | 0 | 22 | 0 |
fig = px.bar(x = prime_video_director_high_movies['Director'][:15],
y = prime_video_director_high_movies['Prime Video'][:15],
color = prime_video_director_high_movies['Prime Video'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Director', 'x' : 'Movies Count'},
title = 'Director with Highest Movies : Prime Video')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
# disney_director_movies = director_data_movies[director_data_movies['Disney+'] != 0].sort_values(by = 'Disney+', ascending = False).reset_index()
# disney_director_movies = disney_director_movies.drop(['index', 'Netflix', 'Hulu', 'Prime Video', 'Movies Count'], axis = 1)
disney_director_high_movies = df_director_high_movies.sort_values(by = 'Disney+', ascending = False).reset_index()
disney_director_high_movies = disney_director_high_movies.drop(['index'], axis = 1)
disney_director_low_movies = df_director_high_movies.sort_values(by = 'Disney+', ascending = True).reset_index()
disney_director_low_movies = disney_director_low_movies.drop(['index'], axis = 1)
disney_director_high_movies.head(5)
| Director | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | Paul Hoen | 15 | 0 | 0 | 0 | 15 |
| 1 | James Algar | 12 | 0 | 0 | 0 | 12 |
| 2 | Robert Stevenson | 14 | 0 | 1 | 2 | 11 |
| 3 | Vincent McEveety | 9 | 0 | 0 | 0 | 9 |
| 4 | Kenny Ortega | 8 | 0 | 0 | 0 | 8 |
fig = px.bar(x = disney_director_high_movies['Director'][:15],
y = disney_director_high_movies['Disney+'][:15],
color = disney_director_high_movies['Disney+'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Director', 'x' : 'Movies Count'},
title = 'Director with Highest Movies : Disney+')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
f, ax = plt.subplots(1, 2 , figsize = (20, 5))
sns.distplot(director_data_movies['Movies Count'], bins = 20, kde = True, ax = ax[0])
sns.boxplot(director_data_movies['Movies Count'], ax = ax[1])
plt.show()
# Creating distinct dataframes only with the movies present on individual streaming platforms
netflix_director_movies = director_data_movies[director_data_movies['Netflix'] != 0].sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_director_movies = netflix_director_movies.drop(['index', 'Hulu', 'Prime Video', 'Disney+', 'Movies Count'], axis = 1)
hulu_director_movies = director_data_movies[director_data_movies['Hulu'] != 0].sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_director_movies = hulu_director_movies.drop(['index', 'Netflix', 'Prime Video', 'Disney+', 'Movies Count'], axis = 1)
prime_video_director_movies = director_data_movies[director_data_movies['Prime Video'] != 0].sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_director_movies = prime_video_director_movies.drop(['index', 'Netflix', 'Hulu', 'Disney+', 'Movies Count'], axis = 1)
disney_director_movies = director_data_movies[director_data_movies['Disney+'] != 0].sort_values(by = 'Disney+', ascending = False).reset_index()
disney_director_movies = disney_director_movies.drop(['index', 'Netflix', 'Hulu', 'Prime Video', 'Movies Count'], axis = 1)
# Defining plot size and title
plt.figure(figsize = (20, 5))
plt.title('Director Movies Count Per Platform')
# Plotting the information from each dataset into a histogram
sns.histplot(disney_director_movies['Disney+'][:50], color = 'darkblue', legend = True, kde = True)
sns.histplot(prime_video_director_movies['Prime Video'][:50], color = 'lightblue', legend = True, kde = True)
sns.histplot(netflix_director_movies['Netflix'][:50], color = 'red', legend = True, kde = True)
sns.histplot(hulu_director_movies['Hulu'][:50], color = 'lightgreen', legend = True, kde = True)
# Setting the legend
plt.legend(['Disney+', 'Prime Video', 'Netflix', 'Hulu'])
plt.show()
print(f'''
The Director with Highest Movies Count Ever Got is '{df_director_high_movies['Director'][0]}' : '{df_director_high_movies['Movies Count'].max()}'\n
The Director with Lowest Movies Count Ever Got is '{df_director_low_movies['Director'][0]}' : '{df_director_low_movies['Movies Count'].min()}'\n
The Director with Highest Movies Count on 'Netflix' is '{netflix_director_high_movies['Director'][0]}' : '{netflix_director_high_movies['Netflix'].max()}'\n
The Director with Lowest Movies Count on 'Netflix' is '{netflix_director_low_movies['Director'][0]}' : '{netflix_director_low_movies['Netflix'].min()}'\n
The Director with Highest Movies Count on 'Hulu' is '{hulu_director_high_movies['Director'][0]}' : '{hulu_director_high_movies['Hulu'].max()}'\n
The Director with Lowest Movies Count on 'Hulu' is '{hulu_director_low_movies['Director'][0]}' : '{hulu_director_low_movies['Hulu'].min()}'\n
The Director with Highest Movies Count on 'Prime Video' is '{prime_video_director_high_movies['Director'][0]}' : '{prime_video_director_high_movies['Prime Video'].max()}'\n
The Director with Lowest Movies Count on 'Prime Video' is '{prime_video_director_low_movies['Director'][0]}' : '{prime_video_director_low_movies['Prime Video'].min()}'\n
The Director with Highest Movies Count on 'Disney+' is '{disney_director_high_movies['Director'][0]}' : '{disney_director_high_movies['Disney+'].max()}'\n
The Director with Lowest Movies Count on 'Disney+' is '{disney_director_low_movies['Director'][0]}' : '{disney_director_low_movies['Disney+'].min()}'\n
''')
The Director with Highest Movies Count Ever Got is 'Jay Chapman' : '36'
The Director with Lowest Movies Count Ever Got is 'Peter Ettedgui' : '1'
The Director with Highest Movies Count on 'Netflix' is 'Jan Suter' : '21'
The Director with Lowest Movies Count on 'Netflix' is 'Jeff Rector' : '0'
The Director with Highest Movies Count on 'Hulu' is 'Savage Steve Holland' : '5'
The Director with Lowest Movies Count on 'Hulu' is 'Jay Chapman' : '0'
The Director with Highest Movies Count on 'Prime Video' is 'Joseph Kane' : '31'
The Director with Lowest Movies Count on 'Prime Video' is 'Özcan Alper' : '0'
The Director with Highest Movies Count on 'Disney+' is 'Paul Hoen' : '15'
The Director with Lowest Movies Count on 'Disney+' is 'Jay Chapman' : '0'
# Distribution of movies director in each platform
plt.figure(figsize = (20, 5))
plt.title('Director with Movies Count for All Platforms')
sns.violinplot(x = director_data_movies['Movies Count'][:100], color = 'gold', legend = True, kde = True, shade = False)
plt.show()
# Distribution of Director Movies Count in each platform
f1, ax1 = plt.subplots(1, 2 , figsize = (20, 5))
sns.violinplot(x = netflix_director_movies['Netflix'][:100], color = 'red', ax = ax1[0])
sns.violinplot(x = hulu_director_movies['Hulu'][:100], color = 'lightgreen', ax = ax1[1])
f2, ax2 = plt.subplots(1, 2 , figsize = (20, 5))
sns.violinplot(x = prime_video_director_movies['Prime Video'][:100], color = 'lightblue', ax = ax2[0])
sns.violinplot(x = disney_director_movies['Disney+'][:100], color = 'darkblue', ax = ax2[1])
plt.show()
print(f'''
Accross All Platforms the Average Movies Count of Director is '{round(director_data_movies['Movies Count'].mean(), ndigits = 2)}'\n
The Average Movies Count of Director on 'Netflix' is '{round(netflix_director_movies['Netflix'].mean(), ndigits = 2)}'\n
The Average Movies Count of Director on 'Hulu' is '{round(hulu_director_movies['Hulu'].mean(), ndigits = 2)}'\n
The Average Movies Count of Director on 'Prime Video' is '{round(prime_video_director_movies['Prime Video'].mean(), ndigits = 2)}'\n
The Average Movies Count of Director on 'Disney+' is '{round(disney_director_movies['Disney+'].mean(), ndigits = 2)}'\n
''')
Accross All Platforms the Average Movies Count of Director is '1.48'
The Average Movies Count of Director on 'Netflix' is '1.29'
The Average Movies Count of Director on 'Hulu' is '1.09'
The Average Movies Count of Director on 'Prime Video' is '1.37'
The Average Movies Count of Director on 'Disney+' is '1.57'
print(f'''
Accross All Platforms Total Count of Director is '{director_data_movies['Director'].unique().shape[0]}'\n
Total Count of Director on 'Netflix' is '{netflix_director_movies['Director'].unique().shape[0]}'\n
Total Count of Director on 'Hulu' is '{hulu_director_movies['Director'].unique().shape[0]}'\n
Total Count of Director on 'Prime Video' is '{prime_video_director_movies['Director'].unique().shape[0]}'\n
Total Count of Director on 'Disney+' is '{disney_director_movies['Director'].unique().shape[0]}'\n
''')
Accross All Platforms Total Count of Director is '12760'
Total Count of Director on 'Netflix' is '3174'
Total Count of Director on 'Hulu' is '1066'
Total Count of Director on 'Prime Video' is '9909'
Total Count of Director on 'Disney+' is '473'
plt.figure(figsize = (20, 5))
sns.lineplot(x = director_data_movies['Director'][:10], y = director_data_movies['Netflix'][:10], color = 'red')
sns.lineplot(x = director_data_movies['Director'][:10], y = director_data_movies['Hulu'][:10], color = 'lightgreen')
sns.lineplot(x = director_data_movies['Director'][:10], y = director_data_movies['Prime Video'][:10], color = 'lightblue')
sns.lineplot(x = director_data_movies['Director'][:10], y = director_data_movies['Disney+'][:10], color = 'darkblue')
plt.xlabel('Director', fontsize = 20)
plt.ylabel('Movies Count', fontsize = 20)
plt.show()
fig, axes = plt.subplots(2, 2, figsize = (20 , 10))
n_d_ax1 = sns.lineplot(y = director_data_movies['Director'][:10], x = director_data_movies['Netflix'][:10], color = 'red', ax = axes[0, 0])
h_d_ax2 = sns.lineplot(y = director_data_movies['Director'][:10], x = director_data_movies['Hulu'][:10], color = 'lightgreen', ax = axes[0, 1])
p_d_ax3 = sns.lineplot(y = director_data_movies['Director'][:10], x = director_data_movies['Prime Video'][:10], color = 'lightblue', ax = axes[1, 0])
d_d_ax4 = sns.lineplot(y = director_data_movies['Director'][:10], x = director_data_movies['Disney+'][:10], color = 'darkblue', ax = axes[1, 1])
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
n_d_ax1.title.set_text(labels[0])
h_d_ax2.title.set_text(labels[1])
p_d_ax3.title.set_text(labels[2])
d_d_ax4.title.set_text(labels[3])
plt.show()
fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
n_d_ax1 = sns.barplot(y = netflix_director_movies['Director'][:10], x = netflix_director_movies['Netflix'][:10], palette = 'Reds_r', ax = axes[0, 0])
h_d_ax2 = sns.barplot(y = hulu_director_movies['Director'][:10], x = hulu_director_movies['Hulu'][:10], palette = 'Greens_r', ax = axes[0, 1])
p_d_ax3 = sns.barplot(y = prime_video_director_movies['Director'][:10], x = prime_video_director_movies['Prime Video'][:10], palette = 'Blues_r', ax = axes[1, 0])
d_d_ax4 = sns.barplot(y = disney_director_movies['Director'][:10], x = disney_director_movies['Disney+'][:10], palette = 'BuPu_r', ax = axes[1, 1])
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
n_d_ax1.title.set_text(labels[0])
h_d_ax2.title.set_text(labels[1])
p_d_ax3.title.set_text(labels[2])
d_d_ax4.title.set_text(labels[3])
plt.show()
# Defining plot size and title
plt.figure(figsize = (20, 5))
plt.title('Director Movies Count Per Platform')
# Plotting the information from each dataset into a histogram
sns.kdeplot(netflix_director_movies['Netflix'][:10], color = 'red', legend = True)
sns.kdeplot(hulu_director_movies['Hulu'][:10], color = 'green', legend = True)
sns.kdeplot(prime_video_director_movies['Prime Video'][:10], color = 'lightblue', legend = True)
sns.kdeplot(disney_director_movies['Disney+'][:10], color = 'darkblue', legend = True)
# Setting the legend
plt.legend(['Netflix', 'Hulu', 'Prime Video', 'Disney+'])
plt.show()
fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
n_d_ax1 = sns.barplot(y = director_data_movies['Director'][:10], x = director_data_movies['Netflix'][:10], palette = 'Reds_r', ax = axes[0, 0])
h_d_ax2 = sns.barplot(y = director_data_movies['Director'][:10], x = director_data_movies['Hulu'][:10], palette = 'Greens_r', ax = axes[0, 1])
p_d_ax3 = sns.barplot(y = director_data_movies['Director'][:10], x = director_data_movies['Prime Video'][:10], palette = 'Blues_r', ax = axes[1, 0])
d_d_ax4 = sns.barplot(y = director_data_movies['Director'][:10], x = director_data_movies['Disney+'][:10], palette = 'BuPu_r', ax = axes[1, 1])
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
n_d_ax1.title.set_text(labels[0])
h_d_ax2.title.set_text(labels[1])
p_d_ax3.title.set_text(labels[2])
d_d_ax4.title.set_text(labels[3])
plt.show()
df_movies_mixed_directors.drop(df_movies_mixed_directors.loc[df_movies_mixed_directors['Directors'] == "NA"].index, inplace = True)
# df_movies_mixed_directors = df_movies_mixed_directors[df_movies_mixed_directors.Director != "NA"]
df_movies_mixed_directors.drop(df_movies_mixed_directors.loc[df_movies_mixed_directors['Number of Directors'] == 1].index, inplace = True)
df_movies_mixed_directors.head(5)
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | ... | Plotline | Runtime | Kind | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | Number of Directors | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 2 | The Matrix | 1999 | 16 | 8.7 | 88 | Lana Wachowski,Lilly Wachowski | Keanu Reeves,Laurence Fishburne,Carrie-Anne Mo... | Action,Sci-Fi | United States | ... | Thomas A. Anderson is a man living two lives. ... | 136 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | 2 |
| 2 | 3 | Avengers: Infinity War | 2018 | 13 | 8.4 | 85 | Anthony Russo,Joe Russo | Robert Downey Jr.,Chris Hemsworth,Mark Ruffalo... | Action,Adventure,Sci-Fi | United States | ... | As the Avengers and their allies have continue... | 149 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | 2 |
| 5 | 6 | Spider-Man: Into the Spider-Verse | 2018 | 7 | 8.4 | 97 | Bob Persichetti,Peter Ramsey,Rodney Rothman | Shameik Moore,Jake Johnson,Hailee Steinfeld,Ma... | Animation,Action,Adventure,Family,Sci-Fi | United States | ... | Phil Lord and Christopher Miller, the creative... | 117 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | 3 |
| 14 | 15 | Monty Python and the Holy Grail | 1975 | 7 | 8.2 | 97 | Terry Gilliam,Terry Jones | Graham Chapman,John Cleese,Eric Idle,Terry Gil... | Adventure,Comedy,Fantasy | United Kingdom | ... | History is turned on its comic head when, in t... | 91 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | 2 |
| 35 | 36 | Klaus | 2019 | 7 | 8.2 | 94 | Sergio Pablos,Carlos Martínez López | Jason Schwartzman,J.K. Simmons,Rashida Jones,W... | Animation,Adventure,Comedy,Family | Spain,United Kingdom,United States | ... | When Jesper (Jason Schwartzman) distinguishes ... | 96 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | 2 |
5 rows × 21 columns
mixed_directors_count = df_movies_mixed_directors.groupby('Directors')['Title'].count()
mixed_directors_movies = df_movies_mixed_directors.groupby('Directors')[['Netflix', 'Hulu', 'Prime Video', 'Disney+']].sum()
mixed_directors_data_movies = pd.concat([mixed_directors_count, mixed_directors_movies], axis = 1).reset_index().rename(columns = {'Title' : 'Movies Count', 'Directors' : 'Mixed Director'})
mixed_directors_data_movies = mixed_directors_data_movies.sort_values(by = 'Movies Count', ascending = False)
mixed_directors_data_movies.head(5)
| Mixed Director | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 1174 | Raúl Campos,Jan Suter | 20 | 20 | 0 | 0 | 0 |
| 11 | Abbas Alibhai Burmawalla,Mastan Alibhai Burmaw... | 7 | 2 | 0 | 6 | 0 |
| 467 | Ethan Coen,Joel Coen | 6 | 4 | 1 | 2 | 0 |
| 882 | Lana Wachowski,Lilly Wachowski | 5 | 4 | 0 | 1 | 0 |
| 455 | Elizabeth Banks,Steven Brill,Steve Carr,Rusty ... | 5 | 2 | 0 | 4 | 0 |
# Mixed Director with Movies Counts - All Platforms Combined
mixed_directors_data_movies.sort_values(by = 'Movies Count', ascending = False)[:10]
| Mixed Director | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 1174 | Raúl Campos,Jan Suter | 20 | 20 | 0 | 0 | 0 |
| 11 | Abbas Alibhai Burmawalla,Mastan Alibhai Burmaw... | 7 | 2 | 0 | 6 | 0 |
| 467 | Ethan Coen,Joel Coen | 6 | 4 | 1 | 2 | 0 |
| 882 | Lana Wachowski,Lilly Wachowski | 5 | 4 | 0 | 1 | 0 |
| 455 | Elizabeth Banks,Steven Brill,Steve Carr,Rusty ... | 5 | 2 | 0 | 4 | 0 |
| 117 | Anthony Russo,Joe Russo | 5 | 1 | 0 | 1 | 3 |
| 485 | Frank Capra,Anatole Litvak | 5 | 2 | 0 | 5 | 0 |
| 447 | Eduardo Quiroz,Jose Quiroz | 4 | 0 | 0 | 4 | 0 |
| 476 | Fenton Bailey,Randy Barbato | 4 | 0 | 0 | 4 | 0 |
| 1520 | Zack Coffman,Scott Di Lalla | 4 | 0 | 0 | 4 | 0 |
df_mixed_directors_high_movies = mixed_directors_data_movies.sort_values(by = 'Movies Count', ascending = False).reset_index()
df_mixed_directors_high_movies = df_mixed_directors_high_movies.drop(['index'], axis = 1)
# filter = (mixed_directors_data_movies['Movies Count'] = = (mixed_directors_data_movies['Movies Count'].max()))
# df_mixed_directors_high_movies = mixed_directors_data_movies[filter]
# highest_rated_movies = mixed_directors_data_movies.loc[mixed_directors_data_movies['Movies Count'].idxmax()]
print('\nMixed Director with Highest Ever Movies Count are : All Platforms Combined\n')
df_mixed_directors_high_movies.head(5)
Mixed Director with Highest Ever Movies Count are : All Platforms Combined
| Mixed Director | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | Raúl Campos,Jan Suter | 20 | 20 | 0 | 0 | 0 |
| 1 | Abbas Alibhai Burmawalla,Mastan Alibhai Burmaw... | 7 | 2 | 0 | 6 | 0 |
| 2 | Ethan Coen,Joel Coen | 6 | 4 | 1 | 2 | 0 |
| 3 | Lana Wachowski,Lilly Wachowski | 5 | 4 | 0 | 1 | 0 |
| 4 | Elizabeth Banks,Steven Brill,Steve Carr,Rusty ... | 5 | 2 | 0 | 4 | 0 |
fig = px.bar(y = df_mixed_directors_high_movies['Mixed Director'][:15],
x = df_mixed_directors_high_movies['Movies Count'][:15],
color = df_mixed_directors_high_movies['Movies Count'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies', 'x' : 'Number of Mixed Director'},
title = 'Movies with Highest Number of Mixed Directors : All Platforms')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
df_mixed_directors_low_movies = mixed_directors_data_movies.sort_values(by = 'Movies Count', ascending = True).reset_index()
df_mixed_directors_low_movies = df_mixed_directors_low_movies.drop(['index'], axis = 1)
# filter = (mixed_directors_data_movies['Movies Count'] = = (mixed_directors_data_movies['Movies Count'].min()))
# df_mixed_directors_low_movies = mixed_directors_data_movies[filter]
print('\nMixed Director with Lowest Ever Movies Count are : All Platforms Combined\n')
df_mixed_directors_low_movies.head(5)
Mixed Director with Lowest Ever Movies Count are : All Platforms Combined
| Mixed Director | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | Robin Budd,Donovan Cook | 1 | 0 | 0 | 0 | 1 |
| 1 | Andrew Stanton,Lee Unkrich | 1 | 0 | 0 | 0 | 1 |
| 2 | Alex Harvey,Tommy Sowards | 1 | 0 | 0 | 1 | 0 |
| 3 | Alex Kleider,Corey Ogilvie | 1 | 0 | 0 | 1 | 0 |
| 4 | Amber Dawn Lee,Rob Brownstein,Jeff Chassler,Ro... | 1 | 0 | 0 | 1 | 0 |
fig = px.bar(y = df_mixed_directors_low_movies['Mixed Director'][:15],
x = df_mixed_directors_low_movies['Movies Count'][:15],
color = df_mixed_directors_low_movies['Movies Count'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies', 'x' : 'Number of Mixed Director'},
title = 'Movies with Lowest Number of Mixed Directors : All Platforms')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
print(f'''
Total '{df_movies_directors['Directors'].count()}' Titles are available on All Platforms, out of which\n
You Can Choose to see Movies from Total '{mixed_directors_data_movies['Mixed Director'].unique().shape[0]}' Mixed Director, They were Like this, \n
{mixed_directors_data_movies.sort_values(by = 'Movies Count', ascending = False)['Mixed Director'].head(5).unique()} etc. \n
The Mixed Director with Highest Movies Count have '{mixed_directors_data_movies['Movies Count'].max()}' Movies Available is '{df_mixed_directors_high_movies['Mixed Director'][0]}', &\n
The Mixed Director with Lowest Movies Count have '{mixed_directors_data_movies['Movies Count'].min()}' Movies Available is '{df_mixed_directors_low_movies['Mixed Director'][0]}'
''')
Total '16566' Titles are available on All Platforms, out of which
You Can Choose to see Movies from Total '1527' Mixed Director, They were Like this,
['Raúl Campos,Jan Suter'
'Abbas Alibhai Burmawalla,Mastan Alibhai Burmawalla'
'Ethan Coen,Joel Coen' 'Lana Wachowski,Lilly Wachowski'
'Elizabeth Banks,Steven Brill,Steve Carr,Rusty Cundieff,James Duffy,Griffin Dunne,Peter Farrelly,Patrik Forsberg,Will Graham,James Gunn,Brett Ratner,Jonathan van Tulleken,Bob Odenkirk'] etc.
The Mixed Director with Highest Movies Count have '20' Movies Available is 'Raúl Campos,Jan Suter', &
The Mixed Director with Lowest Movies Count have '1' Movies Available is 'Robin Budd,Donovan Cook'
fig = px.pie(mixed_directors_data_movies[:4], names = 'Mixed Director', values = 'Movies Count', color_discrete_sequence = px.colors.sequential.Teal)
fig.update_traces(textposition = 'inside', textinfo = 'percent+label', title = 'Movies Count based on Mixed Director')
fig.show()
# netflix_mixed_directors_movies = mixed_directors_data_movies[mixed_directors_data_movies['Netflix'] != 0].sort_values(by = 'Netflix', ascending = False).reset_index()
# netflix_mixed_directors_movies = netflix_mixed_directors_movies.drop(['index', 'Hulu', 'Prime Video', 'Disney+', 'Movies Count'], axis = 1)
netflix_mixed_directors_high_movies = df_mixed_directors_high_movies.sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_mixed_directors_high_movies = netflix_mixed_directors_high_movies.drop(['index'], axis = 1)
netflix_mixed_directors_low_movies = df_mixed_directors_high_movies.sort_values(by = 'Netflix', ascending = True).reset_index()
netflix_mixed_directors_low_movies = netflix_mixed_directors_low_movies.drop(['index'], axis = 1)
netflix_mixed_directors_high_movies.head(5)
| Mixed Director | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | Raúl Campos,Jan Suter | 20 | 20 | 0 | 0 | 0 |
| 1 | Ethan Coen,Joel Coen | 6 | 4 | 1 | 2 | 0 |
| 2 | Lana Wachowski,Lilly Wachowski | 5 | 4 | 0 | 1 | 0 |
| 3 | Nate Adams,Adam Carolla | 3 | 3 | 0 | 0 | 0 |
| 4 | Michael Simon,Matthew McNeil | 3 | 3 | 0 | 1 | 0 |
# hulu_mixed_directors_movies = mixed_directors_data_movies[mixed_directors_data_movies['Hulu'] != 0].sort_values(by = 'Hulu', ascending = False).reset_index()
# hulu_mixed_directors_movies = hulu_mixed_directors_movies.drop(['index', 'Netflix', 'Prime Video', 'Disney+', 'Movies Count'], axis = 1)
hulu_mixed_directors_high_movies = df_mixed_directors_high_movies.sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_mixed_directors_high_movies = hulu_mixed_directors_high_movies.drop(['index'], axis = 1)
hulu_mixed_directors_low_movies = df_mixed_directors_high_movies.sort_values(by = 'Hulu', ascending = True).reset_index()
hulu_mixed_directors_low_movies = hulu_mixed_directors_low_movies.drop(['index'], axis = 1)
hulu_mixed_directors_high_movies.head(5)
| Mixed Director | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | Don Argott,Sheena M. Joyce | 2 | 0 | 2 | 0 | 0 |
| 1 | Jocelyn DeBoer,Dawn Luebbe | 1 | 0 | 1 | 0 | 0 |
| 2 | Stuart Walker,Mitchell Leisen | 1 | 0 | 1 | 1 | 0 |
| 3 | Alexander Lahl,Max Mönch | 1 | 0 | 1 | 0 | 0 |
| 4 | Steve Jones,Todd Jones | 1 | 0 | 1 | 1 | 0 |
# prime_video_mixed_directors_movies = mixed_directors_data_movies[mixed_directors_data_movies['Prime Video'] != 0].sort_values(by = 'Prime Video', ascending = False).reset_index()
# prime_video_mixed_directors_movies = prime_video_mixed_directors_movies.drop(['index', 'Netflix', 'Hulu', 'Disney+', 'Movies Count'], axis = 1)
prime_video_mixed_directors_high_movies = df_mixed_directors_high_movies.sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_mixed_directors_high_movies = prime_video_mixed_directors_high_movies.drop(['index'], axis = 1)
prime_video_mixed_directors_low_movies = df_mixed_directors_high_movies.sort_values(by = 'Prime Video', ascending = True).reset_index()
prime_video_mixed_directors_low_movies = prime_video_mixed_directors_low_movies.drop(['index'], axis = 1)
prime_video_mixed_directors_high_movies.head(5)
| Mixed Director | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | Abbas Alibhai Burmawalla,Mastan Alibhai Burmaw... | 7 | 2 | 0 | 6 | 0 |
| 1 | Frank Capra,Anatole Litvak | 5 | 2 | 0 | 5 | 0 |
| 2 | Elizabeth Banks,Steven Brill,Steve Carr,Rusty ... | 5 | 2 | 0 | 4 | 0 |
| 3 | Eduardo Quiroz,Jose Quiroz | 4 | 0 | 0 | 4 | 0 |
| 4 | Fenton Bailey,Randy Barbato | 4 | 0 | 0 | 4 | 0 |
# disney_mixed_directors_movies = mixed_directors_data_movies[mixed_directors_data_movies['Disney+'] != 0].sort_values(by = 'Disney+', ascending = False).reset_index()
# disney_mixed_directors_movies = disney_mixed_directors_movies.drop(['index', 'Netflix', 'Hulu', 'Prime Video', 'Movies Count'], axis = 1)
disney_mixed_directors_high_movies = df_mixed_directors_high_movies.sort_values(by = 'Disney+', ascending = False).reset_index()
disney_mixed_directors_high_movies = disney_mixed_directors_high_movies.drop(['index'], axis = 1)
disney_mixed_directors_low_movies = df_mixed_directors_high_movies.sort_values(by = 'Disney+', ascending = True).reset_index()
disney_mixed_directors_low_movies = disney_mixed_directors_low_movies.drop(['index'], axis = 1)
disney_mixed_directors_high_movies.head(5)
| Mixed Director | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | Anthony Russo,Joe Russo | 5 | 1 | 0 | 1 | 3 |
| 1 | Ron Clements,John Musker | 3 | 1 | 0 | 0 | 3 |
| 2 | Robert C. Ramirez,Patrick A. Ventura | 2 | 0 | 0 | 0 | 2 |
| 3 | Chris Buck,Jennifer Lee | 2 | 0 | 0 | 0 | 2 |
| 4 | Saul Blinkoff,Elliot M. Bour | 2 | 0 | 0 | 0 | 2 |
f, ax = plt.subplots(1, 2 , figsize = (20, 5))
sns.distplot(mixed_directors_data_movies['Movies Count'], bins = 20, kde = True, ax = ax[0])
sns.boxplot(mixed_directors_data_movies['Movies Count'], ax = ax[1])
plt.show()
# Creating distinct dataframes only with the movies present on individual streaming platforms
netflix_mixed_directors_movies = mixed_directors_data_movies[mixed_directors_data_movies['Netflix'] != 0].sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_mixed_directors_movies = netflix_mixed_directors_movies.drop(['index', 'Hulu', 'Prime Video', 'Disney+', 'Movies Count'], axis = 1)
hulu_mixed_directors_movies = mixed_directors_data_movies[mixed_directors_data_movies['Hulu'] != 0].sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_mixed_directors_movies = hulu_mixed_directors_movies.drop(['index', 'Netflix', 'Prime Video', 'Disney+', 'Movies Count'], axis = 1)
prime_video_mixed_directors_movies = mixed_directors_data_movies[mixed_directors_data_movies['Prime Video'] != 0].sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_mixed_directors_movies = prime_video_mixed_directors_movies.drop(['index', 'Netflix', 'Hulu', 'Disney+', 'Movies Count'], axis = 1)
disney_mixed_directors_movies = mixed_directors_data_movies[mixed_directors_data_movies['Disney+'] != 0].sort_values(by = 'Disney+', ascending = False).reset_index()
disney_mixed_directors_movies = disney_mixed_directors_movies.drop(['index', 'Netflix', 'Hulu', 'Prime Video', 'Movies Count'], axis = 1)
# Defining plot size and title
plt.figure(figsize = (20, 5))
plt.title('Mixed Director Movies Count Per Platform')
# Plotting the information from each dataset into a histogram
sns.histplot(prime_video_mixed_directors_movies['Prime Video'][:100], color = 'lightblue', legend = True, kde = True)
sns.histplot(netflix_mixed_directors_movies['Netflix'][:100], color = 'red', legend = True, kde = True)
sns.histplot(hulu_mixed_directors_movies['Hulu'][:100], color = 'lightgreen', legend = True, kde = True)
sns.histplot(disney_mixed_directors_movies['Disney+'][:100], color = 'darkblue', legend = True, kde = True)
# Setting the legend
plt.legend(['Prime Video', 'Netflix', 'Hulu', 'Disney+'])
plt.show()
print(f'''
The Mixed Director with Highest Movies Count Ever Got is '{df_mixed_directors_high_movies['Mixed Director'][0]}' : '{df_mixed_directors_high_movies['Movies Count'].max()}'\n
The Mixed Director with Lowest Movies Count Ever Got is '{df_mixed_directors_low_movies['Mixed Director'][0]}' : '{df_mixed_directors_low_movies['Movies Count'].min()}'\n
The Mixed Director with Highest Movies Count on 'Netflix' is '{netflix_mixed_directors_high_movies['Mixed Director'][0]}' : '{netflix_mixed_directors_high_movies['Netflix'].max()}'\n
The Mixed Director with Lowest Movies Count on 'Netflix' is '{netflix_mixed_directors_low_movies['Mixed Director'][0]}' : '{netflix_mixed_directors_low_movies['Netflix'].min()}'\n
The Mixed Director with Highest Movies Count on 'Hulu' is '{hulu_mixed_directors_high_movies['Mixed Director'][0]}' : '{hulu_mixed_directors_high_movies['Hulu'].max()}'\n
The Mixed Director with Lowest Movies Count on 'Hulu' is '{hulu_mixed_directors_low_movies['Mixed Director'][0]}' : '{hulu_mixed_directors_low_movies['Hulu'].min()}'\n
The Mixed Director with Highest Movies Count on 'Prime Video' is '{prime_video_mixed_directors_high_movies['Mixed Director'][0]}' : '{prime_video_mixed_directors_high_movies['Prime Video'].max()}'\n
The Mixed Director with Lowest Movies Count on 'Prime Video' is '{prime_video_mixed_directors_low_movies['Mixed Director'][0]}' : '{prime_video_mixed_directors_low_movies['Prime Video'].min()}'\n
The Mixed Director with Highest Movies Count on 'Disney+' is '{disney_mixed_directors_high_movies['Mixed Director'][0]}' : '{disney_mixed_directors_high_movies['Disney+'].max()}'\n
The Mixed Director with Lowest Movies Count on 'Disney+' is '{disney_mixed_directors_low_movies['Mixed Director'][0]}' : '{disney_mixed_directors_low_movies['Disney+'].min()}'\n
''')
The Mixed Director with Highest Movies Count Ever Got is 'Raúl Campos,Jan Suter' : '20'
The Mixed Director with Lowest Movies Count Ever Got is 'Robin Budd,Donovan Cook' : '1'
The Mixed Director with Highest Movies Count on 'Netflix' is 'Raúl Campos,Jan Suter' : '20'
The Mixed Director with Lowest Movies Count on 'Netflix' is 'Jim Kammerud,Brian Smith,Bill Speers' : '0'
The Mixed Director with Highest Movies Count on 'Hulu' is 'Don Argott,Sheena M. Joyce' : '2'
The Mixed Director with Lowest Movies Count on 'Hulu' is 'Raúl Campos,Jan Suter' : '0'
The Mixed Director with Highest Movies Count on 'Prime Video' is 'Abbas Alibhai Burmawalla,Mastan Alibhai Burmawalla' : '6'
The Mixed Director with Lowest Movies Count on 'Prime Video' is 'Raúl Campos,Jan Suter' : '0'
The Mixed Director with Highest Movies Count on 'Disney+' is 'Anthony Russo,Joe Russo' : '3'
The Mixed Director with Lowest Movies Count on 'Disney+' is 'Raúl Campos,Jan Suter' : '0'
print(f'''
Accross All Platforms the Average Movies Count of Mixed Director is '{round(mixed_directors_data_movies['Movies Count'].mean(), ndigits = 2)}'\n
The Average Movies Count of Mixed Director on 'Netflix' is '{round(netflix_mixed_directors_movies['Netflix'].mean(), ndigits = 2)}'\n
The Average Movies Count of Mixed Director on 'Hulu' is '{round(hulu_mixed_directors_movies['Hulu'].mean(), ndigits = 2)}'\n
The Average Movies Count of Mixed Director on 'Prime Video' is '{round(prime_video_mixed_directors_movies['Prime Video'].mean(), ndigits = 2)}'\n
The Average Movies Count of Mixed Director on 'Disney+' is '{round(disney_mixed_directors_movies['Disney+'].mean(), ndigits = 2)}'\n
''')
Accross All Platforms the Average Movies Count of Mixed Director is '1.1'
The Average Movies Count of Mixed Director on 'Netflix' is '1.12'
The Average Movies Count of Mixed Director on 'Hulu' is '1.01'
The Average Movies Count of Mixed Director on 'Prime Video' is '1.07'
The Average Movies Count of Mixed Director on 'Disney+' is '1.1'
print(f'''
Accross All Platforms Total Count of Mixed Director is '{mixed_directors_data_movies['Mixed Director'].unique().shape[0]}'\n
Total Count of Mixed Director on 'Netflix' is '{netflix_mixed_directors_movies['Mixed Director'].unique().shape[0]}'\n
Total Count of Mixed Director on 'Hulu' is '{hulu_mixed_directors_movies['Mixed Director'].unique().shape[0]}'\n
Total Count of Mixed Director on 'Prime Video' is '{prime_video_mixed_directors_movies['Mixed Director'].unique().shape[0]}'\n
Total Count of Mixed Director on 'Disney+' is '{disney_mixed_directors_movies['Mixed Director'].unique().shape[0]}'\n
''')
Accross All Platforms Total Count of Mixed Director is '1527'
Total Count of Mixed Director on 'Netflix' is '373'
Total Count of Mixed Director on 'Hulu' is '93'
Total Count of Mixed Director on 'Prime Video' is '1047'
Total Count of Mixed Director on 'Disney+' is '104'
plt.figure(figsize = (20, 5))
sns.lineplot(x = mixed_directors_data_movies['Mixed Director'][:5], y = mixed_directors_data_movies['Netflix'][:5], color = 'red')
sns.lineplot(x = mixed_directors_data_movies['Mixed Director'][:5], y = mixed_directors_data_movies['Hulu'][:5], color = 'lightgreen')
sns.lineplot(x = mixed_directors_data_movies['Mixed Director'][:5], y = mixed_directors_data_movies['Prime Video'][:5], color = 'lightblue')
sns.lineplot(x = mixed_directors_data_movies['Mixed Director'][:5], y = mixed_directors_data_movies['Disney+'][:5], color = 'darkblue')
plt.xlabel('Mixed Director', fontsize = 15)
plt.ylabel('Movies Count', fontsize = 15)
plt.show()
fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
n_d_ax1 = sns.barplot(x = mixed_directors_data_movies['Mixed Director'][:10], y = mixed_directors_data_movies['Netflix'][:10], palette = 'Reds_r', ax = axes[0, 0])
h_d_ax2 = sns.barplot(x = mixed_directors_data_movies['Mixed Director'][:10], y = mixed_directors_data_movies['Hulu'][:10], palette = 'Greens_r', ax = axes[0, 1])
p_d_ax3 = sns.barplot(x = mixed_directors_data_movies['Mixed Director'][:10], y = mixed_directors_data_movies['Prime Video'][:10], palette = 'Blues_r', ax = axes[1, 0])
d_d_ax4 = sns.barplot(x = mixed_directors_data_movies['Mixed Director'][:10], y = mixed_directors_data_movies['Disney+'][:10], palette = 'BuPu_r', ax = axes[1, 1])
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
n_d_ax1.title.set_text(labels[0])
h_d_ax2.title.set_text(labels[1])
p_d_ax3.title.set_text(labels[2])
d_d_ax4.title.set_text(labels[3])
plt.show()
fig, axes = plt.subplots(2, 2, figsize = (20 , 10))
n_md_ax1 = sns.lineplot(x = mixed_directors_data_movies['Mixed Director'][:10], y = mixed_directors_data_movies['Netflix'][:10], color = 'red', ax = axes[0, 0])
h_md_ax2 = sns.lineplot(x = mixed_directors_data_movies['Mixed Director'][:10], y = mixed_directors_data_movies['Hulu'][:10], color = 'lightgreen', ax = axes[0, 1])
p_md_ax3 = sns.lineplot(x = mixed_directors_data_movies['Mixed Director'][:10], y = mixed_directors_data_movies['Prime Video'][:10], color = 'lightblue', ax = axes[1, 0])
d_md_ax4 = sns.lineplot(x = mixed_directors_data_movies['Mixed Director'][:10], y = mixed_directors_data_movies['Disney+'][:10], color = 'darkblue', ax = axes[1, 1])
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
n_md_ax1.title.set_text(labels[0])
h_md_ax2.title.set_text(labels[1])
p_md_ax3.title.set_text(labels[2])
d_md_ax4.title.set_text(labels[3])
plt.show()
# Defining plot size and title
plt.figure(figsize = (20, 5))
plt.title('Mixed Director Movies Count Per Platform')
# Plotting the information from each dataset into a histogram
sns.kdeplot(netflix_mixed_directors_movies['Netflix'][:50], color = 'red', legend = True)
sns.kdeplot(hulu_mixed_directors_movies['Hulu'][:50], color = 'green', legend = True)
sns.kdeplot(prime_video_mixed_directors_movies['Prime Video'][:50], color = 'lightblue', legend = True)
sns.kdeplot(disney_mixed_directors_movies['Disney+'][:50], color = 'darkblue', legend = True)
# Setting the legend
plt.legend(['Netflix', 'Hulu', 'Prime Video', 'Disney+'])
plt.show()
fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
n_md_ax1 = sns.barplot(x = netflix_mixed_directors_movies['Mixed Director'][:10], y = netflix_mixed_directors_movies['Netflix'][:10], palette = 'Reds_r', ax = axes[0, 0])
h_md_ax2 = sns.barplot(x = hulu_mixed_directors_movies['Mixed Director'][:10], y = hulu_mixed_directors_movies['Hulu'][:10], palette = 'Greens_r', ax = axes[0, 1])
p_md_ax3 = sns.barplot(x = prime_video_mixed_directors_movies['Mixed Director'][:10], y = prime_video_mixed_directors_movies['Prime Video'][:10], palette = 'Blues_r', ax = axes[1, 0])
d_md_ax4 = sns.barplot(x = disney_mixed_directors_movies['Mixed Director'][:10], y = disney_mixed_directors_movies['Disney+'][:10], palette = 'BuPu_r', ax = axes[1, 1])
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
n_md_ax1.title.set_text(labels[0])
h_md_ax2.title.set_text(labels[1])
p_md_ax3.title.set_text(labels[2])
d_md_ax4.title.set_text(labels[3])
plt.show()
fig = go.Figure(go.Funnel(y = mixed_directors_data_movies['Mixed Director'][:10], x = mixed_directors_data_movies['Movies Count'][:10]))
fig.show()